Intrinsic motor network connectivity predicts corticospinal excitability

Abstract

AbstractState-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of inter-subject and inter-session variability of NIBS effects. While these approaches have concentrated on functional states defined as local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in brain networks. Here, we investigate the hypothesis that EEG-derived long-range resting-state connectivity of the primary motor cortex (M1) in the pre-stimulation period is largely congruent with the motor network (MN) and that the state of this network modulates the responses evoked by transcranial magnetic stimulation (TMS) of M1.One thousand suprathreshold TMS pulses were delivered to left M1 in 8 subjects at rest, while simultaneously recording EEG, and measuring motor evoked potentials (MEP) from the right hand to estimate corticospinal excitability. Source-space functional connectivity of left M1 to the whole-brain was assessed using the imaginary part of the Phase Locking Value at the individual peak frequency of the 9-13 Hz sensorimotor μ-rhythm in a 1-second window before the TMS pulse.Group-averaged connectivity map revealed that left M1 is functionally linked to regions within the MN, namely the left supplementary motor area and the right M1. On average, TMS pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to pulses delivered at low connectivity states (p=0.02, one-tail paired-samplet-test).At single-subject level, we demonstrate that this positive relation between the connectivity state of the motor network and corticospinal excitability is clearly expressed in those subjects that feature an overall large cortico-spinal excitability, paving the way for individualized connectivity-informed state-dependent stimulation.HighlightsEEG pre-stimulus connectivity of left M1 largely corresponds to the motor networkStronger motor network (MN) connectivity corresponds to greater MEP amplitudesLinear regression models based on MN connectivity can predict MEP amplitude